Determinants of excessive daytime sleepiness in two First Nation communities
Bibliographic record
Abstract
BACKGROUND: Excessive daytime sleepiness may be determined by a number of factors including personal characteristics, co-morbidities and socio-economic conditions. In this study we identified factors associated with excessive daytime sleepiness in 2 First Nation communities in rural Saskatchewan. METHODS: Data for this study were from a 2012-13 baseline assessment of the First Nations Lung Health Project, in collaboration between two Cree First Nation reserve communities in Saskatchewan and researchers at the University of Saskatchewan. Community research assistants conducted the assessments in two stages. In the first stage, brochures describing the purpose and nature of the project were distributed on a house by house basis. In the second stage, all individuals age 17 years and older not attending school in the participating communities were invited to the local health care center to participate in interviewer-administered questionnaires and clinical assessments. Excessive daytime sleepiness was defined as Epworth Sleepiness Scale score > 10. RESULTS: Of 874 persons studied, 829 had valid Epworth Sleepiness Scale scores. Of these, 91(11.0%) had excessive daytime sleepiness; 12.4% in women and 9.6% in men. Multivariate logistic regression analysis indicated that respiratory comorbidities, environmental exposures and loud snoring were significantly associated with excessive daytime sleepiness. CONCLUSIONS: Excessive daytime sleepiness in First Nations peoples living on reserves in rural Saskatchewan is associated with factors related to respiratory co-morbidities, conditions of poverty, and loud snoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".